Mineral analysis systems are used to determine the composition of samples. Examples of such mineral analysis systems include the QEMSCAN® (Quantitative Evaluation of Minerals by Scanning electron microscopy) and MLA (Mineral Liberation Analyzer) from FEI Company, Hillsboro, Oreg., the assignee of the present invention.
The sample, typically in the form of small granules fixed in epoxy in a 30 mm diameter sample block, is placed in a vacuum chamber. An electron beam is directed toward a series of dwell points on the sample and, in a process called “energy dispersive x-ray spectroscopy” or “EDS,” the energies of x-rays coming from the each point of the sample in response to the electron beam are measured and plotted in a histogram to form a spectrum represented of the composition of that point. As the electron beam is scanned across the sample surface, a spectrum can be collected at each point on the scan. Because the electron beam cannot be deflected sufficiently to scan the entire width of the sample block, the sample block is divided into tiles, with the size of the tiles limited by the maximum deflection of the electron beam. A tile is scanned with the beam being addressed to each dwell point in the tile, and then the sample stage is moved and a subsequent tile is scanned until the entire sample block has been scanned. FIG. 10 shows a sample block 1002 divided into tiles 1004, each tile having multiple dwell points 1006. FIG. 10 is not drawn to scale.
A compositional map of the sample can be compiled, with each scanned point, or dwell point, on the sample corresponding to a pixel on the compositional map. The dwell points on the sample are also sometimes referred to as pixels. Each element produces a unique X-ray spectrum that is characteristic of the element's unique atomic structure. A measured spectrum can be compared to a library of known reference spectra of various elements to determine which elements and minerals are present. The measured spectrum is compared to combinations of known spectra of elements and minerals to determine the composition at each point, as the point can include multiple minerals. Determining the component spectra that make-up of a measured x-ray spectrum composed of multiple materials is referred to as “deconvolution” of the spectrum.
Backscattered electron (BSE) detectors are also used for mineral analysis in conjunction with x-ray detectors. The intensity of the BSE signal is a function of the average atomic number of material under the electron beam, and this relationship can be used in mineral identification.
Determining the mineral phases that are represented by the x-ray spectrum from a point of unknown composition is computationally intensive. A “mineral phase” is used herein includes not only minerals, but also elements in pure form. A typical prior art approach to material classification attempts to match the experimentally obtained x-ray spectrum from each point on a sample with combinations of reference spectra from library of spectra of known minerals. A difference metric is computed to represent the degree of similarity between the measured data and combinations of known spectra. In one approach, the measured spectrum is compared with every possible combination of spectra from a reference list to determine the best match as indicated by the lowest difference metric. Because the reference list can contain a large number of reference spectra, comparing every possible combination of reference spectra requires a substantial processing time.
In some mineral classification algorithms, increasing the number of reference spectra causes a non-linear increase in overall analysis time. Processing times can increase exponentially as the number of spectra in the spectral reference list increases. While processing time can be reduced by limiting the number of reference spectra considered to a subset of the most common minerals, limiting the number of reference spectra can result in an important sample-specific phase remaining unclassified, due to the absence of one or more components in the reference list. This can result in the classification algorithm being unable to identify a mineral phase, or misidentifying a specific phase.
Another problem related to the size of the spectra library is that the accuracy and speed of spectra deconvolution is negatively impacted by chemically similar reference spectra. For some points on the sample, the electron beam interrogation volume comprises multiple minerals, and will produce a convoluted spectrum, that is, a mixture of two or more spectra overlaid upon each other. In the prior art, deconvolution is performed without any assumptions as to the possible composition of the mixture. A combination of any two or more spectra from the reference list is considered possible, and all such possible combinations are compared with the experimentally obtained spectrum to determine the best match. As a spectral reference list grows, the probability of misclassification increases there are more possibilities for incorrect matching.
For example, FIG. 1 is an illustration of a mineral distribution image 100 taken from a sample. The image can be generated by scanning the sample with a high energy beam, and measuring the energy distribution of x-rays emitted from the sample as a function of scan position. On a per pixel basis, these energy distributions can be fitted and/or compared to a known reference catalog 102 of energy distributions obtained from pure elements and pure minerals in order to identify the minerals in the sample at each scanned position. Different colors can be assigned to different minerals in the catalog 102, and an image of the spatial mineral distribution in the sample can be generated by plotting the colors of identified minerals as a function of scanned positions. Techniques for identifying minerals based on a catalog of elemental x-ray spectra are disclosed, for example, in AU2009212187 for “Method and System for Spectrum Data Analysis” of Corbett et al., U.S. Pat. No. 8,937,282 for “Mineral Identification Using Mineral Definition Including Variability” of Owen et al., and U.S. patent application Ser. No. 14/073,523, filed Nov. 6, 2013, for “Sub-pixel Analysis and Display of Fine Grained Mineral Samples” of Owen et al., all of which are herein incorporated by reference in their entirety. The energy spectrum collected from each pixel is compared to the spectra of pure elements and pure minerals in catalog 102 resulting in an undesirably long computing time. Additionally, comparing the sample on a per pixel basis to catalog 102 increases the chance for misidentification. As an example, area 104 shows a clay region made up of a convoluted mixture of minerals that are difficult to verify because the spectra of the convoluted mixture of elements can be misidentified as a material or mixture with a similar spectra. Area 106 shows a region incorrectly identified as magnesite (light blue), rather than the correct identification of magnesium-rich dolomite.